基于综合用电煤耗的电力高质量发展评价指标研究及应用

煤电平均供电煤耗作为评价能源系统效率的指标已在行业内广泛应用。但该项指标无法很好地反映一个国家、地区的用电结构与用电能耗水平。为此，参照行业内普遍采用的火电厂发电煤耗和供电煤耗指标，定义“综合用电煤耗”，即用户每用1 kW?h电“消耗”的标准煤量（单位:g/(kW?h)）。在此基础上，提出“综合发电煤耗”与“综合供电煤耗”的概念，形成完整的指标评价体系。对近年典型省份指标值的研究表明，该指标体系能够很好地反映供给侧国家、省区或企业的发电结构与电能生产能耗水平。这一指标体系能够为正确理解和计算用电、输电与电能替代项目的化石能源消耗量及其排放量提供依据。     

采编播系统的异地构建和应用

主要阐述在距离台技术大楼2 km远的海盐一家门频道采、编、播系统的构建。整个系统的搭建,充分考虑了时间、空间、距离等各方面的要求,突出异地构建的特点,融合了电视台的节目录制、安全播出、计算机网络、远程控制等多方面技术。     

A method for rapidly evaluating reliability and predicting remaining useful life using two-dimensional convolutional neural network with signal conversion

Journal of Mechanical Science and Technology - Real-time monitoring and rapid evaluation of bearing operating conditions, especially for the reliability evaluation and remaining useful life (RUL)...     

Preparation of CaO granules using the granulation method

ABSTRACT

Improving the hydration resistance of CaO particle in manufacturing and application of free CaO-containing materials has practical significance. In this study, CaO granules was made from Ca(OH)₂ particles, which were fabricated by the granulation method. The results showed that the hydration resistance of the CaO granules which was prepared under 1700?r?min^?1 was the best, the CaO granules was sintered well in calcination process, the shell of CaO granules was relatively dense, which improves the hydration resistance of CaO granules, and the rate of hydration weight increment was 0.58% after placed in the air for 20 days under a temperature of 10–14°C and a relative humidity of 57–81%.     

深度学习识别光网络单元故障的设计与应用

为解决依赖装维上门鉴别光网络单元故障带来的不便,可以从机器视觉入手实现自动化故障识别。近年,ImageNet挑战赛的成功推动了物体识别技术的跨越式发展,特别是基于卷积的深度学习技术在视觉识别方面已经达到人类水平,为光网络单元故障的自动识别提供了技术基础。文章对识别光网络单元的工作状态进行了研究,将设备工作状态分为7个场景,提出了利用手机APP采集图片识别故障的解决方案并投入了实际生产;重点阐述了深度学习模块的设计与实现,提出一种通过算法整合的方式综合运用物体检测和图像分类算法,分3阶段逐步求精,解决了图片过滤,光网络单元型号和状态识别等问题,实现了基于计算机视觉自动识别光网络单元故障。从数据上看产品的端到端准确率超过84%,识别速度达到10 FPS,月均提供服务超过1万人次,在减少用户等待的同时节约了人力资源。